Mood Tracking of Radio Station Broadcasts

  • Jacek Grekow
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8502)


This paper presents an example of a system for the analysis of emotions contained within radio broadcasts. We prepared training data, did feature extraction, built classifiers for music/speech discrimination and for emotion detection in music. To study changes in emotions, we used recorded broadcasts from 4 selected European radio stations. The collected data allowed us to determine the dominant emotion in the radio broadcasts and construct maps visualizing the distribution of emotions in time. The obtained results provide a new interesting view of the emotional content of radio station broadcasts.


Emotion detection Mood tracking Audio feature extraction Music information retrieval Radio broadcasts 


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  1. 1.
    Li, T., Ogihara, M.: Detecting emotion in music. In: Proceedings of the Fifth International Symposium on Music Information Retrieval, pp. 239–240 (2003)Google Scholar
  2. 2.
    Grekow, J., Raś, Z.W.: Detecting emotions in classical music from MIDI files. In: Rauch, J., Raś, Z.W., Berka, P., Elomaa, T. (eds.) ISMIS 2009. LNCS (LNAI), vol. 5722, pp. 261–270. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  3. 3.
    Lu, L., Liu, D., Zhang, H.J.: Automatic mood detection and tracking of music audio signals. IEEE Transactions on Audio, Speech and Language Processing 14(1), 5–18 (2006)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Song, Y., Dixon, S., Pearce, M.: Evaluation of Musical Features for Emotion Classification. In: Proceedings of the 13th International Society for Music Information Retrieval Conference (2012)Google Scholar
  5. 5.
    Yang, Y.-H., Lin, Y.C., Su, Y.F., Chen, H.H.: A regression approach to music emotion recognition. IEEE Transactions on Audio, Speech, and Language Processing 16(2), 448–457 (2008)CrossRefGoogle Scholar
  6. 6.
    Schmidt, E., Kim, Y.: Modeling Musical Emotion Dynamics with Conditional Random Fields. In: Proceedings of the 12th International Society for Music Information Retrieval Conference, pp. 777–782 (2011)Google Scholar
  7. 7.
    Schmidt, E.M., Turnbull, D., Kim, Y.E.: Feature Selection for Content-Based, Time-Varying Musical Emotion Regression. In: Proc. ACM SIGMM International Conference on Multimedia Information Retrieval, Philadelphia, PA (2010)Google Scholar
  8. 8.
    Schmidt, E.M., Kim, Y.E.: Prediction of time-varying musical mood distributions from audio. In: Proceedings of the 2010 International Society for Music Information Retrieval Conference, Utrecht, Netherlands (2010)Google Scholar
  9. 9.
    Grekow, J.: Mood tracking of musical compositions. In: Chen, L., Felfernig, A., Liu, J., Raś, Z.W. (eds.) ISMIS 2012. LNCS (LNAI), vol. 7661, pp. 228–233. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Grekow, J., Raś, Z.W.: Emotion based MIDI files retrieval system. In: Raś, Z.W., Wieczorkowska, A.A. (eds.) Advances in Music Information Retrieval. SCI, vol. 274, pp. 261–284. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Bachorik, J.P., Bangert, M., Loui, P., Larke, K., Berger, J., Rowe, R., Schlaug, G.: Emotion in motion: Investigating the time-course of emotional judgments of musical stimuli. Music Perception 26(4), 355–364 (2009)CrossRefGoogle Scholar
  12. 12.
    Xiao, Z., Dellandrea, E., Dou, W., Chen, L.: What is the best segment duration for music mood analysis? In: International Workshop on Content-Based Multimedia Indexing (CBMI 2008), pp. 17–24 (2008)Google Scholar
  13. 13.
    Schmidt, E.M., Scott, J.J., Kim, Y.E.: Feature Learning in Dynamic Environments: Modeling the Acoustic Structure of Musical Emotion. In: Proceedings of the 12th International Society for Music Information Retrieval Conference, pp. 325–330 (2012)Google Scholar
  14. 14.
    Yang, Y.H., Homer, H., Chen, H.H.: Machine Recognition of Music Emotion: A Review. ACM Transactions on Intelligent Systems and Technology 3(6), Article No. 40 (2012)Google Scholar
  15. 15.
    Kim, Y., Schmidt, E., Migneco, R., Morton, B., Richardson, P., Scott, J., Speck, J., Turnbull, D.: State of the Art Report: Music Emotion Recognition: A State of the Art Review. In: Proceedings of the 11th International Society for Music Information Retrieval Conference, pp. 255–266 (2010)Google Scholar
  16. 16.
    Mohammad, S.: From Once Upon a Time to Happily Ever After: Tracking Emotions in Novels and Fairy Tales. In: Proceedings of the ACL 2011 Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities, Portland, OR, USA, pp. 105–114 (2011)Google Scholar
  17. 17.
    Yeh, J.-H., Pao, T.-L., Pai, C.-Y., Cheng, Y.-M.: Tracking and Visualizing the Changes of Mandarin Emotional Expression. In: Huang, D.-S., Wunsch II, D.C., Levine, D.S., Jo, K.-H. (eds.) ICIC 2008. LNCS, vol. 5226, pp. 978–984. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  18. 18.
    Lidy, T., Rauber, A.: Visually Profiling Radio Stations. In: Proceedings of the 7th International Conference on Music Information Retrieval (2006)Google Scholar
  19. 19.
    Thayer, R.E.: The biopsychology arousal. Oxford University Press (1989)Google Scholar
  20. 20.
    Tzanetakis, G., Cook, P.: Marsyas: A framework for audio analysis. Organized Sound 10, 293–302 (2000)Google Scholar
  21. 21.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explorations, 11(1) (2009)Google Scholar
  22. 22.
    Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, San Francisco (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jacek Grekow
    • 1
  1. 1.Faculty of Computer ScienceBialystok University of TechnologyBialystokPoland

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